| Literature DB >> 30051244 |
Amir Manbachi1,2, Tharindu De Silva1, Ali Uneri1, Matthew Jacobson1, Joseph Goerres1, Michael Ketcha1, Runze Han1, Nafi Aygun3, David Thompson4, Xiaobu Ye2, Sebastian Vogt5, Gerhard Kleinszig5, Camilo Molina2, Rajiv Iyer2, Tomas Garzon-Muvdi2, Michael R Raber2, Mari Groves2, Jean-Paul Wolinsky2, Jeffrey H Siewerdsen6,7,8,9,10.
Abstract
Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon's decision) and (b) Active Assistant (labels presented before the surgeon's decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.Entities:
Keywords: Clinical translation; Image-guided surgery; Intraoperative imaging; LevelCheck; Spine surgery; Surgical workflow
Mesh:
Year: 2018 PMID: 30051244 PMCID: PMC9107073 DOI: 10.1007/s10439-018-2099-2
Source DB: PubMed Journal: Ann Biomed Eng ISSN: 0090-6964 Impact factor: 4.219